Approximately 400,000-500,000 patients remain chronically symptomatic every year following mild or moderate traumatic brain injury (mmTBI) according to latest estimates. To optimally treat these patients, we must first understand the underlying neuropathological changes after injury rather than relying on clinical observations and patient-reported symptoms (i.e. current clinical practice). Our central hypothesis is that structural damage to white matter following injury will be functionally expressed as a deficiency in the long-distance EEG signals that underlie cognitive control over behavior. Our preliminary work establishes that theta band synchrony underlies various forms of cognitive control, providing a common mechanism for understanding the most prevalent deficits (i.e. distractibility, impulsivity, irritability) following injury. This work will capitalize on our recent findings that white matter abnormalities are reliably present in mmTBI patients and contribute to deficiencies in cognitive control. To test our central hypotheses, 100 mmTBI patients (18-55 years) will be recruited from the Departments of Neurosurgery and Emergency Medicine from our local hospitals. All patients will undergo a thorough neurobehavioral exam during the early semi-acute (<2 weeks), late semi-acute (2 months) and early chronic (four months) injury stages. Advanced behavioral measures of cognitive control developed at NIH (EXAMINER battery) and recommended measures from Common Data Elements will be used to characterize neurobehavioral deficits. Electrophysiology (EEG) will be used to characterize theta band synchrony during cognitive control and high angular resolution diffusion imaging (HARDI) will be used to determine white matter abnormalities between the main nodes of the cognitive control network. Finally, in addition to CT scans, extensive anatomical imaging (T1, T2, FLAIR and SWI) will be conducted to identify patients with focal lesions. The current grant is innovative both in our multimodal longitudinal approach, as well as two of our selected biomarkers (white matter and EEG synchrony) for understanding cognitive control deficits in mmTBI. Novel data analytic techniques (pattern classifiers) will be applied to objectively determine the bias-free predictive power of these biomarkers on the course of recovery. Following this study, clinicians will be able to understand the neuronal mechanisms mediating a failure to recover following mmTBI, and ultimately utilize these biomarkers to determine which patient will require additional rehabilitative services. This represents a crucial first step for improving diagnosis and developing novel therapeutic options, key components for other projects on our COBRE application.